diff --git a/flux_train_network.py b/flux_train_network.py
index 65b121e7c..fce62ddc3 100644
--- a/flux_train_network.py
+++ b/flux_train_network.py
@@ -16,6 +16,7 @@
 
 setup_logging()
 import logging
+from safetensors.torch import load_file
 
 logger = logging.getLogger(__name__)
 
@@ -24,6 +25,7 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
     def __init__(self):
         super().__init__()
         self.sample_prompts_te_outputs = None
+        self.tokenizer_cache = {}
 
     def assert_extra_args(self, args, train_dataset_group):
         super().assert_extra_args(args, train_dataset_group)
@@ -82,17 +84,35 @@ def load_target_model(self, args, weight_dtype, accelerator):
             model = self.prepare_split_model(model, weight_dtype, accelerator)
 
         clip_l = flux_utils.load_clip_l(args.clip_l, weight_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors)
-        clip_l.eval()
-
         # if the file is fp8 and we are using fp8_base (not unet), we can load it as is (fp8)
         if args.fp8_base and not args.fp8_base_unet:
             loading_dtype = None  # as is
         else:
             loading_dtype = weight_dtype
 
+        def inject_embedding(model, tokenizer, placeholder, embed_file, embed_key):
+            embed_state_dict = load_file(embed_file)
+            if not embed_key in embed_state_dict:
+                raise Exception(f"{embed_key} not found in {embed_file}")
+            tokenizer.add_tokens(placeholder)
+            index = tokenizer.convert_tokens_to_ids(placeholder)
+            if (model.get_input_embeddings().num_embeddings <= len(tokenizer)):
+                model.resize_token_embeddings(len(tokenizer))
+                logger.info(f"Expanded model embeddings to : {model.get_input_embeddings().num_embeddings}")
+            model.get_input_embeddings().weight.data[index] = embed_state_dict[embed_key]
+            logger.info(f"Added custom embedding for {placeholder} to {embed_key} as token {index}")
+
         # loading t5xxl to cpu takes a long time, so we should load to gpu in future
         t5xxl = flux_utils.load_t5xxl(args.t5xxl, loading_dtype, "cpu", disable_mmap=args.disable_mmap_load_safetensors)
+        
+        if args.additional_embedding:
+            for placeholder, embed_file in args.additional_embedding:
+                    inject_embedding(clip_l, self.get_tokenize_strategy(args).clip_l, placeholder, embed_file, "clip_l")
+                    inject_embedding(t5xxl, self.get_tokenize_strategy(args).t5xxl, placeholder, embed_file, "t5xxl")
+        
+        clip_l.eval()
         t5xxl.eval()
+        
         if args.fp8_base and not args.fp8_base_unet:
             # check dtype of model
             if t5xxl.dtype == torch.float8_e4m3fnuz or t5xxl.dtype == torch.float8_e5m2 or t5xxl.dtype == torch.float8_e5m2fnuz:
@@ -142,7 +162,11 @@ def prepare_split_model(self, model, weight_dtype, accelerator):
         return flux_lower
 
     def get_tokenize_strategy(self, args):
+        
         name = self.get_flux_model_name(args)
+        cache_key = name + "_" + str(args.t5xxl_max_token_length)
+        if cache_key in self.tokenizer_cache:
+            return self.tokenizer_cache[cache_key]
 
         if args.t5xxl_max_token_length is None:
             if name == "schnell":
@@ -153,7 +177,9 @@ def get_tokenize_strategy(self, args):
             t5xxl_max_token_length = args.t5xxl_max_token_length
 
         logger.info(f"t5xxl_max_token_length: {t5xxl_max_token_length}")
-        return strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length, args.tokenizer_cache_dir)
+        tokenizer = strategy_flux.FluxTokenizeStrategy(t5xxl_max_token_length, args.tokenizer_cache_dir)
+        self.tokenizer_cache[cache_key] = tokenizer
+        return tokenizer
 
     def get_tokenizers(self, tokenize_strategy: strategy_flux.FluxTokenizeStrategy):
         return [tokenize_strategy.clip_l, tokenize_strategy.t5xxl]
@@ -508,6 +534,11 @@ def setup_parser() -> argparse.ArgumentParser:
         help="[EXPERIMENTAL] use split mode for Flux model, network arg `train_blocks=single` is required"
         + "/[実験的] Fluxモデルの分割モードを使用する。ネットワーク引数`train_blocks=single`が必要",
     )
+    parser.add_argument(
+        "--additional_embedding",
+        action="append",
+        nargs=2
+    )
     return parser